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After posting ALL of my ramblings on prusak.com for almost 6 years, I’ve finally created a dedicated blog just for my professional postings about web analytics, split testing, optimization, internet marketing, etc.

Have you ever used the map overlay report in Google Analytics?
It’s a fairly straight forward report that provides site metrics broken down by geographic region.

When viewing data for the United States, you get a row of data for each state.
Most of the metrics provide useful information if the visitor’s state has any significance to your business.

For example, visitors from Ohio might have a significantly lower conversion rate than other states, prompting you to adjust you online campaigns accordingly.

There is one metric though that’s alway’s annoyed me.
That metric is Visits.

The Visits metric simply tells you the number of visits to your site, and in our case, the number of visits from a specific state.
The problem is that in it’s raw form, this number doesn’t really provide any value.

Looking at a site that is evenly popular throughout the United States, you’ll usually see these states as the top four:

California

Texas

New York

Florida

Guess what? These states are the states with the largest population.

What I really want to see is how popular a site is per state, factoring in the actual state population.

Lets call this metric State Popularity.

In the past I simply downloaded the visits per state data into an excel spreadsheet and divided the number of visits by the state population. Crude, but it works.

I was discussing this with Jeremy Aube who wrote the fantastic Google Analytics Report Enhancer (or G.A.R.E) plugin for FireFox. I asked him if he could add State Popularity functionality to GARE and a few days later he did it!

Here’s what it the report looks like with the new version of the GARE plugin:

The stats above are for a site that provides reviews and cheats for video games.

Looking at California (#2) and Washington (#7) it becomes obvious that although the site has almost three times more visitors from California than from Washington, I would guess that Washington has more gamers per-capita than California.

While I love GWO, the underlying assumption is that you can create the HTML for your tests on your own.
For many of the smaller mom-and-pop sites, simply creating the test pages or sections is beyond their capabilities.
Both of these new products come with a built in WYSIWYG editor, which truly lowers the barrier of entry for split testing.

Both products look promising and you should try them out.
One thing I really liked about Visual Web Optimizer is that it will grab your current page and then let you create variations based on your existing page.

Both products are in private beta but I got a hold of some invite codes.

The invite code you can use for Visual Website Optimizer is carsonified

I was reading a simple case study today.
They were testing two different versions of a banner that was advertising a webinar.
One of the banners had an image of the presenter, while the other did not.
The banner without the image of the presenter won (by over 50%).

One of the comments was something along the lines of:
I guess this audience prefers banners without an image of a person.

*sigh*

If you don’t immediately realize the mistake the commenter made, don’t feel bad. It’s a very common mistake.

Beyond the fact that a specific banner (which did have have an image of the presenter) won over a different specific banner (which did not have an image of the presenter) you really can’t be sure of anything.

The loosing banner might have won with:

An image of a different person

A different image of the same person

The same image of the same person in a different position or size on the banner.

The same image of the same person in the same position and size but with different elements on the banner changed.

The point is:

Don’t jump to generalized conclusions based on the outcome of a specific experiment.

Out of the box, MVT experiments with GWO will serve up all possible permutations for an experiment.

Sometimes you want to create sections that are related to each other. For example - a header and footer section that need to be coordinated with each other. The red header needs to be displayed with the red footer and the blue header needs to be displayed with the blue footer.

There is a very simple solution to this.

Just create the experiment as usual and right after you launch the experiment use GWO’s pruning function to remove the combinations you don’t want to serve up (i.e. the red header with the blue footer and the blue header with the red footer).